Item Recommendation in Collaborative Tagging Systems

Along with the new opportunities introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the "noise." Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. Experimental comparison, using data from a real collaborative tagging system (Last.fm), against both recent tag-aware and traditional (non tag aware) item recommendation algorithms indicates significant improvements in recommendation quality. Moreover, the experimental results illustrate the advantage of the proposed hybrid scheme.